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test_overall_performance_multitree = read_tsv("/Users/noa/Workspace/raxml_deep_learning_results/ready_raw_data/Pandit/ML/test_multi_tree_data.tsv")

overall_performance_multitree = read_tsv("/Users/noa/Workspace/raxml_deep_learning_results/ready_raw_data/Pandit/ML/overall_performance_on_test_set.tsv") 

validation_multi_tree = read_tsv("/Users/noa/Workspace/raxml_deep_learning_results/ready_raw_data/Pandit/ML/validation_multi_tree_data.tsv")


default_performance = read_tsv("/Users/noa/Workspace/raxml_deep_learning_results/ready_raw_data/Pandit/ML/default_sampling.tsv")

test_overall_performance_multitree = read_tsv("/Users/noa/Workspace/raxml_deep_learning_results/ready_raw_data/Pandit/ML/overall_performance_on_test_set.tsv")

New names:Rows: 2556 Columns: 101── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr  (4): msa_path, starting_tree_object, starting_tree_type, metric
dbl (95): ...1, Unnamed: 0, starting_tree_ind, spr_radius, spr_cutoff, spr_radius.1, spr_cutoff.1, predicted_calibrated_fa...
lgl  (2): starting_tree_bool, equal_to_default_config
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

test_overall_performance_multitree

overall_performance_multitree %>% distinct (msa_path) %>% arrange(msa_path)

data<-overall_performance_multitree %>% filter (metric=='predicted_total_accuracy_calibrated_isotonic') %>% filter (threshold>=0.9) %>% distinct (msa_path, total_actual_time, total_time_predicted, status) %>% mutate(time_saved = 20/total_actual_time)
data%>% ggplot(aes(x=time_saved))+geom_histogram()
summary(data %>% pull (time_saved))

mean_default_performance<-default_performance %>% group_by(msa_path) %>% summarise(default_status = mean(default_status))


joint_data<-test_overall_performance_multitree %>% group_by(msa_path) %>% summarise(maxi = max(status)) %>% filter (maxi==0) %>% inner_join(mean_default_performance, by = "msa_path") 
joint_data %>% arrange(-default_status)
#mean(joint_data %>% pull (default_status))



joint_data<-test_overall_performance_multitree %>% group_by(msa_path) %>% summarise(maxi = max(status)) %>% filter (maxi==1) %>% inner_join(mean_default_performance, by = "msa_path") 
joint_data %>% arrange(default_status)
#mean(joint_data %>% pull (default_status))


mean(mean_default_performance %>% pull (default_status)) 

mean(test_overall_performance_multitree %>% filter (n_trees_used==40) %>% pull (status)) 

mean_default_performance %>% distinct (msa_path) %>% arrange(msa_path)

overall_performance_multitree %>% distinct (msa_path) %>% arrange(msa_path)

validation_multi_tree %>% distinct (msa_path) %>% arrange(msa_path)


test_overall_performance_multitree %>% distinct (msa_path) %>% arrange(msa_path)

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